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The Analysis of Time Series: An Introduction
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In this paper, simple descriptive techniques for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.Abstract:
Simple descriptive techniques probability models for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.read more
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Journal ArticleDOI
Detection model for mastitis in cows milked in an automatic milking system.
R.M. de Mol,W. Ouweltjes +1 more
TL;DR: In this article, a detection model for mastitis in dairy cows using time-series models for two variables (milk yield and electrical conductivity of milk), with interpolation on previous values is presented.
Book ChapterDOI
Mathematical Modeling of Infectious Diseases Dynamics
TL;DR: The reader is introduced to the most important notions of epidemic modeling based on the presentation of the classic models as well as the very basic SIR epidemiological model.
Journal ArticleDOI
Comparison of parameter estimation methods for detecting climate-induced changes in productivity of Pacific salmon (Oncorhynchus spp.)
TL;DR: The Pacific salmon (Oncorhynchus spp.) populations can experience persistent changes in productivity, possibly due to climatic shifts as discussed by the authors, and management agencies need to rapidly and reliably detect such changes.
Journal ArticleDOI
Robust maximum likelihood training of heteroscedastic probabilistic neural networks
Zheng Rong Yang,Sheng Chen +1 more
TL;DR: A robust statistical technique known as the Jack-knife is combined with the EM algorithm to provide a robust ML training algorithm and an artificial-data case, the two-dimensional XOR problem, and a real- data case, success or failure prediction of UK private construction companies, are used to evaluate the performance of this robust learning algorithm.
Journal ArticleDOI
Effects of cell size and configuration in cellular automata based prey–predator modelling
Qiuwen Chen,Arthur E. Mynett +1 more
TL;DR: The principal spatial scale of the studied ecosystem is proposed to be used as CA model cell size and to apply the Moore type cell configuration and methods for identifying principal spatial scales have been developed and are presented here.